In an effort to improve the efficiency and scalability of single-image super-resolution (SISR) applications, we introduce AnySR, to rebuild existing arbitrary-scale SR methods into any-scale, any-resource implementation. As a contrast to off-the-shelf methods that solve SR tasks across various scales with the same computing costs, our AnySR innovates in: 1) building arbitrary-scale tasks as any-resource implementation, reducing resource requirements for smaller scales without additional parameters; 2) enhancing any-scale performance in a feature-interweaving fashion, inserting scale pairs into features at regular intervals and ensuring correct feature/scale processing. The efficacy of our AnySR is fully demonstrated by rebuilding most existing arbitrary-scale SISR methods and validating on five popular SISR test datasets. The results show that our AnySR implements SISR tasks in a computing-more-efficient fashion, and performs on par with existing arbitrary-scale SISR methods. For the first time, we realize SISR tasks as not only any-scale in literature, but also as any-resource. Code is available at https://github.com/CrispyFeSo4/AnySR.

翻译：为提高单图像超分辨率（SISR）应用的效率与可扩展性，我们提出了AnySR，旨在将现有任意尺度超分辨率方法重构为任意尺度、任意资源的实现。与现有方法以相同计算成本处理不同尺度超分辨率任务不同，我们的AnySR在以下方面实现创新：1）将任意尺度任务构建为任意资源实现，在不增加额外参数的前提下降低较小尺度任务的资源需求；2）以特征交织方式增强任意尺度性能，通过定期将尺度对插入特征中，确保特征与尺度的正确处理。我们通过重构大多数现有任意尺度SISR方法并在五个主流SISR测试数据集上进行验证，充分证明了AnySR的有效性。实验结果表明，AnySR能以更高计算效率的方式实现SISR任务，其性能与现有任意尺度SISR方法相当。我们首次在文献中实现了不仅任意尺度、同时任意资源的SISR任务。代码发布于https://github.com/CrispyFeSo4/AnySR。